Bayesian Inference on DPM models has the big issue that DPMs are
infinite dimensional objects. There are substantially two main families
of methods to deal with this problem: the “marginal methods”,
that are based on integrating out the random distribution and the
“conditional methods” that explicitly instantiate the
DP and rely on its stick-breaking representation. One of the most
used conditional methods is the so-called “slice sampler”, introduced
by [1]}. Here we will describe, adapt to our
goals and finally use its efficient version due to [2]}, that in the original paper had been used to sample from DPMs but also from other mixtures based on normalized weights.
